Edible bird nest shape quality assessment using machine vision system
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Institute of Electrical and Electronics Engineers (IEEE)
2013
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my.unimap-267812013-07-17T05:02:52Z Edible bird nest shape quality assessment using machine vision system Fathinul Syahir, Ahmad Sa'ad Ali Yeon, Md Shakaff, Prof. Dr. Ammar, Zakaria Mohd Zulkifly, Abdullah, Dr. Abdul Hamid, Adom, Prof. Dr Ezanuddin, A. A. M. fathinul@unimap.edu.my aliyeon@unimap.edu.my ammarzakaria@unimap.edu.my mezul@eng.usm.my abdhamid@unimap.edu.my Edible bird nest Fourier descriptor Shape analysis Vision system Link to publisher's homepage at http://ieeexplore.ieee.org Swiftlets are birds contained within the four genera Aerodramus, Hydrochous, Schoutedenapus and Collocalia. To date, the bird nest grading is based on weight, shape and size. The inspection and grading for raw edible bird nest were performed visually by expert panels. This conventional method is relying more on human judgments. A Fourier-based shape separation (FD) method was developed from Charge Couple Device (CCD) image data to grade bird nest by its shape and size. FD was able to differentiate different shape such as oval and 'v' shaped depending on the swiftlet species and geographical origin. The Wilks' lambda analysis was invoked to transform and compress the data set comprising of large number of interconnected variables to a reduced set of variates. It can be further used to differentiate bird nest from different geographical origin. Overall, the vision system was able to correctly classify 100% of the V and Oval shaped and 81.3% for each grade in oval shape of the bird nest. 2013-07-17T04:59:04Z 2013-07-17T04:59:04Z 2012-02-08 Working Paper p. 325-329 978-076954668-1 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6169722 http://hdl.handle.net/123456789/26781 en Proceedings of the International Conference on Intelligent Systems Modelling and Simulation (ISMS 2012) Institute of Electrical and Electronics Engineers (IEEE) |
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Edible bird nest Fourier descriptor Shape analysis Vision system |
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Edible bird nest Fourier descriptor Shape analysis Vision system Fathinul Syahir, Ahmad Sa'ad Ali Yeon, Md Shakaff, Prof. Dr. Ammar, Zakaria Mohd Zulkifly, Abdullah, Dr. Abdul Hamid, Adom, Prof. Dr Ezanuddin, A. A. M. Edible bird nest shape quality assessment using machine vision system |
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Link to publisher's homepage at http://ieeexplore.ieee.org |
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fathinul@unimap.edu.my |
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fathinul@unimap.edu.my Fathinul Syahir, Ahmad Sa'ad Ali Yeon, Md Shakaff, Prof. Dr. Ammar, Zakaria Mohd Zulkifly, Abdullah, Dr. Abdul Hamid, Adom, Prof. Dr Ezanuddin, A. A. M. |
format |
Working Paper |
author |
Fathinul Syahir, Ahmad Sa'ad Ali Yeon, Md Shakaff, Prof. Dr. Ammar, Zakaria Mohd Zulkifly, Abdullah, Dr. Abdul Hamid, Adom, Prof. Dr Ezanuddin, A. A. M. |
author_sort |
Fathinul Syahir, Ahmad Sa'ad |
title |
Edible bird nest shape quality assessment using machine vision system |
title_short |
Edible bird nest shape quality assessment using machine vision system |
title_full |
Edible bird nest shape quality assessment using machine vision system |
title_fullStr |
Edible bird nest shape quality assessment using machine vision system |
title_full_unstemmed |
Edible bird nest shape quality assessment using machine vision system |
title_sort |
edible bird nest shape quality assessment using machine vision system |
publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
publishDate |
2013 |
url |
http://dspace.unimap.edu.my/xmlui/handle/123456789/26781 |
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1643795054147403776 |
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13.18916 |